Situation
In Lab 07, the objective was to test to see if the reflectance sensors were accurately measuring the distance that the AEV travels. Part of the team was tasked with preparing and running the AEV while the others worked on writing and troubleshooting the code. Data was taken from both the AEV and the measuring tape alongside the track and used to calculate marks error and other performance data. The marks error is important in identifying the quality of the reflectance sensors. If the error is too large the sensors could be sending the AEV to a location off from what the programming intends.
Results and Analysis
From the data collected in lab, the team calculated a marks error of one percent, falling well within the acceptable range. This means that the current sensors being used are working properly and will not negatively impact the design of the code. The data also noted how much the AEV moves after the motors have been ordered to shut off. From what was gathered, a significant amount of distance is covered once the motors shut off, about four meters. With this data, the team will be able to predict how much the AEV will move after the brake command is issued, allowing for easier and more accurate code creation. After all the data the lab group has collected from labs, the lab group has come to conclusion of a basic model concerning weight distribution, speed, braking, and consistency. As for the distribution of weight the group has decided to go with a wide model in order to keep the AEV stable when going around the curved edges of the track. This will help the AEV go faster and allow the AEV to hold off braking until the last moment in order to increase the repeatability of the trials. The AEV is to start off with a slow acceleration and gradually build until it hits a certain position which is when the motors reverse and the propellers spin again further braking the AEV from its fast velocity. In testing we found that this system of braking allows the AEV to stop within the sensors on a consistent basis. The lab group has also narrowed down the design concepts to two main AEV models, both consist of wide bases to keep balance around the turns. The designs are different in regards to how the weight is distributed. The first design has a longer body that distributes the weight front to back along the sides of the craft, parallel to the track, and the second having the wings be wider and the weight distributed along the sides, perpendicular to the track. These final designs were chosen due to the only major difference being the weight distribution and everything else remains unchanged. The second design is a little more compact as the first because it is shorter than the second. The lab group hopes to determine which model is more energy efficient in hopes of choosing the proper design.
Balance describes how well the AEV can stay level when moving along the track as well as the weight ratio of the AEV from front to back. Durability describes how well the AEV can withstand continual use without any maintenance. Cost describes how much the AEV will be to construct and still remain effective. Appearance describes the overall aesthetic appeal of the AEV. Efficiency describes how well the AEV makes use of its available power. Accessibility describes how easy it is to get to the components of the AEV and be able to manipulate them as needed. Maintenance describes how often the AEV needs to be fixed and how easy it is to do so. By looking at the scoring sheet, we would continue to use the reference design because it scored significantly higher than the combination design. The most important criteria are cost and appearance and the reference design scored higher and this trend continues with the other criterion. The group will stick with the reference design.
Takeaways
- AEV – The team adjusted the code to make the AEV fit closer between the sensors
- AEV – The team will account for the motion of the AEV after the motors have stopped
- General – Make sure to save all files appropriately before leaving lab to assure no loss of data
Concept Scoring
